Method And Apparatus To Classify Structures In An Image

ABSTRACT

Disclosed is a system and method for segmentation of selected data. In various embodiments, automatic segmentation of fiber tracts in an image data may be performed. The automatic segmentation may allow for identification of specific fiber tracts in an image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application includes subject matter similar to that disclosures inU.S. patent application Ser. No. ______, (Attorney Docket No.5074A-000211) and U.S. patent application Ser. No. ______, (AttorneyDocket No. 5074A-000212). The entire disclosures of each of the aboveapplications are incorporated herein by reference.

FIELD

The subject disclosure relates to a tractography, and particularly to atractography classification method and system.

BACKGROUND

This section provides background information related to the presentdisclosure which is not necessarily prior art.

An imaging technique may be used to acquire image data of a subject.Image data of a subject can include one or more types of data tovisualize one or more structures of the subject. Structures may includeexternal or internal structures of the subject. In various systems, forexample, an image may be acquired of an internal structure of a subjectwith a selective imaging technique.

The image data may be used to generate images that are displayed forviewing by a user. The images may be displayed on a selected system,such as a display device, for a visual inspection by a user. Generally,the user may view the images to assist in performing a procedure on thesubject.

SUMMARY

This section provides a general summary of the disclosure, and is not acomprehensive disclosure of its full scope or all of its features.

A method may be used to identify and/or classify various structures indata, such as an image data. The structures may be identified as fibersor tracts, such as in a brain of a subject, such as a human subject. Itis understood, however, that the process may be used to identify tractsin any appropriate subject. Generally, the tracts are neuronal tractsthat include white matter axons. The method may be used to identifyvarious tracts in a brain to assist in various procedures, such as tumorresection, implant placement (e.g. deep brain stimulation leads), orother appropriate procedures.

In various embodiments, the process may be used to identify neuronaltracts even in the presence of an aberration of standard anatomy. Forexample, a tumor may grow in a brain, which may affect generallyunderstood or present neuronal tracts. Therefore, the process may assistin identifying neuronal tracts even in the presence of a tumor or otherabnormal anatomy in the image data.

The neuronal tracts may then be displayed for various purposes. Thetracts may be used to identify various features and/or structures of thebrain to assist in a procedure. Further, the images may be used toassist in performing a procedure, such as tumor resection or implantpositioning in a selected subject. Accordingly, the image data may beused to identify tracts in the image data and the tracts and/or otherappropriate features may be displayed with the display device in one ormore images.

Further areas of applicability will become apparent from the descriptionprovided herein. The description and specific examples in this summaryare intended for purposes of illustration only and are not intended tolimit the scope of the present disclosure.

DRAWINGS

The drawings described herein are for illustrative purposes only ofselected embodiments and not all possible implementations, and are notintended to limit the scope of the present disclosure.

FIG. 1 is an environmental view of an imaging system and subject;

FIG. 2 is an environmental view of a procedure room;

FIG. 3 is a flow chart of a method, according to various embodiments, ofsegmenting image data;

FIG. 4 is a method, according to various embodiments, of segmentingimage data;

FIG. 5 is a flow chart of a method of segmenting image data, accordingto various embodiments;

FIG. 6A is a flow chart of a method to train a system to segment imagedata, according to various embodiments; and

FIG. 6B is a flow chart of a method to use a system to segment imagedata, according to various embodiments.

Corresponding reference numerals indicate corresponding parts throughoutthe several views of the drawings.

DETAILED DESCRIPTION

Example embodiments will now be described more fully with reference tothe accompanying drawings.

A system may be used to acquire image data of a subject. For example, asschematically illustrated in FIG. 1. Generally, a subject may be imagedwith a selected imaging system 20. The imaging system 20 may be anyappropriate imaging system to acquire a selected image data of a subject24, such as a magnetic resonance imager (MRI). The MRI may operate invarious manners or modes, such as including a diffusion weighted dataacquisition manner. In a diffusion weighted technique, the diffusion ormovement of water may be determined or estimated to assist indetermining tracts, as discussed further herein. The imaging system 20may include a selected support 28 to support the subject 24 duringmovement through the imaging system 20. The imaging system may includecomponents generally known to those skilled in the art. It isunderstood, however, that any appropriate MRI imaging system may be usedas the imaging system 20, or any other appropriate imaging system.Nevertheless, the imaging system 20 may be operated to generate imagedata of the subject 24.

With continuing reference to FIG. 1 and additional reference to FIG. 2,the subject 24, either after acquiring the data, which may includeanatomical image data and/or diffusion weighted data, with the imagingsystem 20 and/or during acquisition (e.g. with an appropriate imagingsystem) may be moved or positioned into a selected operating theater forperforming a procedure. While or to plan for performing a procedure onthe subject 24, an image 32 of the subject 24 may be displayed on adisplay device 36 of a selected workstation or system 40. The display ofthe image 32 may be used for various reasons, such as preparing for aprocedure on the subject 24, assisting in performing a procedure on thesubject 24, or other appropriate purposes. As discussed herein, forexample, the image 32 may include illustration of tracts identified inthe image 32 and/or the image data acquired with the imaging system 24for display on the display device 36. The tracts may include fiberneuronal tracts in the brain of the subject 24. The tracts may alsoinclude other appropriate tracts, such as fiber tracts in a spinal cord.An anatomical or known fiber tract may also be referred to as a givenfiber tract.

The workstation or processor system 40 may include various portions orfeatures, such as an input device 44, a processor portion or module 48and a memory 52. A selected connection, such as a wired or wirelessconnection 54 may connect the processor 48 and/or the memory 52 with thedisplay device 36 and/or the input device 44. Therefore, a selecteduser, such as a user 58 may provide input or select input for the system40 and/or view the display device 36, such as a view of the image 32.

In various embodiments, the image 32 may be registered to the subject 24for a selected procedure. For example, a tumor resection, implantplacement, or the like may be performed on the subject 24. It isunderstood that the subject 24, however, may be an inanimate object andother appropriate procedures may be performed, such as a partreplacement or connection. Imaging and registration may occur in anappropriate manner, such as those disclosed in U.S. Pat. No. 9,311,335,issued Apr. 12, 2016, incorporated herein by reference. Briefly,registration may include a translation between subject space definedrelative to the subject 24 and image space defined by the image 32. Acoordinate system may be developed for both spaces and a translation mapmay be made therebetween.

In various embodiments, for registration, portions of the subject 24 maybe identified, such as by the user 52, and identical portions may beidentified in the image 32. A registration between the subject space andthe image space may then be made by generating or determining atranslation map between the subject space and the image space based uponthe identified similar or identical points, also referred to as fiducialpoints. The processor module 48 may execute instructions to perform thetranslation and registration of the subject space to the image space.

After registration a navigation system 70, which may incorporate theworkstation or processor system 40, may selectively perform thetranslation and registration that may be used to assist in performing aprocedure. Once registration occurs a dynamic reference frame or patientreference device 74 may be connected or fixed to the subject 24. The DRF74 may include a tracking device or portion that is tracked with thenavigation system 70 to determine movement of the subject 24 andmaintain registration with the image 32. An instrument tool 76 may alsobe tracked with the selected tracking device, such as a tracking device78 associate with the instrument, such as attached thereto, for aprocedure. The instrument may be at least one of a deep brainstimulation lead that may be implanted. In addition to the instrument,selected procedures may also be tracked and/or determined such ascomputing a volume of activation for a DBS lead and/or computing athermal ablation estimate for an ablation. In various embodiments, alocalizer 82 may generate a field 86 that is sensed by the trackingdevice 78 and/or DRF 74 to allow for tracking with the navigation system70. A controller, such as a coil array controller 90 may receive asignal from the localizer 82 and/or control the localizer 82 and/orreceive a signal from the tracking device 78 and transmit it to theworkstation processor module 48. The processor module 48 may thenexecute instructions to determine a location of the tracking device 78relative to the subject 24 to allow for navigation of the instrument 76relative to the subject 74. Thus, a graphical representation 94 of thetracked portion (e.g. instrument 76) may be displayed on the displaydevice 36 relative to the image 32 to display a real-time position ofthe instrument 76 relative to the subject 24 by displaying the graphicalrepresentation 94 of the instrument relative to the image 32. Generally,the tracking system is able to track and illustrate a position or poseof the tracked portion. The position may include three dimensionallocation and one or more dimensions orientation. Generally, at leastfour degrees of freedom information, including at least six degrees offreedom, or any selected amount.

As discussed further herein, the image 32 may include selected imagefeatures, such as tracts of the brain, and the graphical representation94 may be displayed relative thereto. Accordingly, defining ordetermining tracts for performing a procedure may assist in performing aselected procedure, as discussed further herein.

As discussed above, the imaging system 20 may be used to generate imagedata of the subject 24 for a selected procedure. The Image data may beprocessed, according to the processes, including one or more processes,as discussed further herein. The image data, following a selectedprocess or plurality of processes, may be used to generate one or moreimages for display on the display device 36. Further, the variousprocesses may be used to identify features that may also be output by aselected processing system, such as the processor module 48, asdiscussed herein.

Disclosed herein, according to various embodiments, are systems andmethods to identify, such as by segmentation, image or data portions.For example, one or more fiber bundles or tract may be segmented. Afiber tract may include one or more fibers that are segmented by asystem that are intended to represent the neuronal tract in a subject'sbrain. A fiber or line includes an individual line produced by aselected system (e.g. algorithm) that may be one or a part of a set oflines, intended to represent the physical neuronal tract in a subject'sbrain. A white matter tract or anatomical tract is a physical bundle ofaxons in the subject's brain that convey electrical signals from oneregion of the brain to another and is intended to be represented by afiber tract. There may be more than one fiber tract and white mattertract in any given data. A given fiber tract or white matter tract maybe a predetermined or named tract.

With initial reference to FIG. 3, and continuing reference to FIGS. 1and 2, a method of identifying or segmenting fiber tracts or bundles isillustrated in the process 100. The process may be used to identify,such as by segmenting selected fiber tracts, which may also be referredto as given fiber tracts. The fiber tracts may be brain fiber tracts andalso referred to as given fiber neuronal tracts. The segmentation mayinclude segmenting (i.e. classifying which fibers belong to a givenneuronal tract) the given tracts from other tracts and/or other data,such as image data. It is understood that the segmentation may occuraccording to various embodiments, including combinations thereof, asdiscussed herein.

Initially, the process 100 may start in start block 104. Starting in thestart block 104 may include appropriate measures or steps, such asassessing a patient, positioning a patient or a subject for imaging,evaluating a subject, or evaluating a possible treatment or diagnosis ofa subject. It is understood, however, that in start block 104 anyappropriate procedures may occur. After starting the process 100 instart block 104, diffusion weighted gradient images or appropriate datamay be accessed or recalled in block 108. It is understood that theprocess 100 may include acquiring image data, such as with the imagingsystem 20, in block 108.

In various embodiments, the image data may be acquired at anyappropriate time and saved in a selected manner to assist in performinga procedure. Therefore, accessing or recalling the image data in block108 may include acquiring image data of the subject with an imagingsystem, such as the imaging system 20, as discussed above and/orrecalling previously acquired image data. For example, image data may beacquired at a first instance, such as during an initial diagnosing ortesting process and this data may be used in the procedure 100.Accordingly, the image data may be accessed in block 108 rather thanacquired and new. Also, or alternatively, image data may be acquired ofthe subject 24 during or in a procedure location (e.g. operating roomfor a resection or implantation).

After the image data is accessed in block 108, a determination of aselected tractography may occur in block 112. The determination of theselected tractography in block 112 may include a whole imagetractography including identifying all possible fiber lines or tractsgiven the acquired image data according to one or more various knowntechniques. Various techniques may include identifying or attempting toidentify all possible tracts with or by a selected algorithm and/oruser. For example, the tractography may be performed according toappropriate techniques, such as appropriate algorithmic techniques. Forexample, a tractography may be performed with a diffusion tensor imagingtractography system such as the tractography system included with theStealthViz® tractography and image guided surgery system, sold byMedtronic, Inc. Other appropriate tractography techniques can includethat described in U.S. Pat. No. 9,311,335, issued Apr. 12, 2016.

In various embodiments, in addition or alternatively to an entire orwhole image tractography (e.g. an entire brain tractography), a selectedtractography may be or include less than a whole image tractography. Theselected tractography may, therefore, include a subset of a whole imagetractography. For example, between only selected regions of interest(e.g. half of a brain, or in a brain stem), etc. Thus, a selectedtractography generated for a segmentation/identification process, asdiscussed herein, may include a whole image tractography and/or only asubset of a whole image tractography. Further, while a whole imagetractography may be generated, only a subset of the fiber lines ortracts may be analyzed for segmentation. Accordingly, discussion hereinto the image tractography in block 112 or equivalents is understood toinclude a whole image tractography, a selected image tractography, orcombinations thereof unless specifically stated otherwise.

After determining the selected tractography in block 112, the selectedtractography may be output in block 116. In various embodiments, theimage may include an image of a brain of the subject 24, such as a humansubject. Accordingly, the whole image tractography may includetractography of a whole brain. A selected tractography may be a portionthereof, as discussed above. It is understood, however, that the brainor image tractography output in block 116 may include simply or only adetermination of possible connection of all tracts. As is understood byone skilled in the art, the tractography may include a determination ofa possible or average movement or tendency of movement of a selectedmaterial, such as water. Therefore, the tractography output in block 116may simply be all possible tracts or connections and/or selected tractsidentified within the accessed image data 108. Therefore, thetractography output in block 116 may not define or identify, eithercompletely or with accuracy, particular anatomical neuronal tractswithin the subject.

The output tractography may then be segmented to output or identifyselected or given fiber tracts, such as neuronal tracts (e.g.corticospinal tracts, optical tracts, etc.) by recalling a trainedclassification system in block 120. The trained classification systemmay include a random forest classification system that has been trainedto use various features, such as fiber points and image features, toclassify whether fibers in a selected tractography belong to specificneuronal tracts. The brain tractography, as discussed above, may includeany identified tracts or connections of possible or average diffusion ofa material, such as water within the image. Accordingly, thesegmentation of the image tractography may attempt to identify orclassify various possible tracts relating to certain anatomicalfunctions or features, such as the corticospinal tracts (CST), orbitalor eye tract, or other appropriate anatomical or neurological tracts.

The classification system may be trained using various parameters thatmay be learned parameters for different tracts. Parameters of featuresmay include various parameters of features that are identifiable and maybe evaluated within the image tractography. Features may includeidentified points and their locations on the tracts in the imagetractography. Line segments between the points and/or vectors from eachpoint. Fractional anisotropy (FA) at each point. Diffusion encoded color(DEC) values at each point. Curvature at each point, such as of the linesegment to the next point. A comparison to an atlas of known fibertracts or known brain regions at the determined points or selectednumber of points. An atlas may be a generally known atlas of selectedfiber tracts (e.g. a brain neuronal tract atlas) or known brain regions.A distance or length of the tracts from respective starting regions toending regions. Additional or other image properties at each pointand/or anatomical gradient magnitudes within the image. These featuresmay be trained with a plurality of data, such as previously classifieddata. The previously classified data may be classified by a readingexpert (e.g. a neurosurgeon).

The trained classification system may then be used to classify tractswithin the image tractography 116. For example, instructions may besaved and recalled from the memory 52 including one or more of thefeatures noted above. The image tractography may then be evaluated withthe classification system to identify given tracts. The segmented tractsmay also be referred to as identified or segmented tracts.

The trained classification system recalled in block 120 is used forsegmenting given fiber tracts. Automatic segmentation of the imagetractography may then occur in block 130. As discussed above, the systemmay include a workstation or processor system 40 that may include theprocessor module 48. The processor module 48 may be in communicationwith the memory 52. The memory 52 may include the accessed image data108 and the classification system that may be recalled in block 120.

The processor module 48 executes or evaluates the accessed data fromblock 108 with the trained classification system to the braintractography to automatically segment fiber tracts, such as neuraltracts or neural fibers, in the image tractography in block 130.Automatic segmentation may occur by applying the classification systemto the selected image tractography. The automatic segmentation appliesthe classification parameters to the selected tractography and,therefore, may generate or determine a selected image segmentation(including a whole or subset segmentation). In various embodiments, theautomatic segmentation may identify selected features, such as of aregion of interest (ROI) (e.g. possible start and ending regions) orother brain areas that the tract passes or doesn't pass through and usethose to classify the fibers. In classifying tracts, the classificationsystem may identify the given fiber tract which may be or is a neuronaltract, i.e. anatomical feature tract. The anatomical feature may bepreselected by a user, such as the user 78, or may be identified by asystem such that all possible tracts are segmented and identified.

The automatic segmentation in bock 130 may be performed by evaluatingthe selected image tractography with the processor module 48 byexecuting the instructions and the recalled classification system. Theautomatic segmentation therefore may determine or identify segmentedfiber tracts may be output in block 140. Outputting the segmented fibertracts in block 140 may include outputting all fiber tracts that have aselected confidence interval to be identified as a selected tract basedupon the classification system recalled in block 120. The output inblock 140 may include only those tracts selected to be identified by auser 78, or other appropriate system, and/or may include identificationand output of all possible tracts from the classification system 120.Accordingly, the classification system 120 may be trained to identify aplurality of tracts (e.g. CST, optical, auditory, etc.) and all trainedtracts may be segmented and output in block 140. Therefore, the output140 may include all tracts which the system is trained and/or onlyselected tracts based upon a selection of the user 78 or otherappropriate selection.

The method 100 may then end in block 150. Ending in block 150, theselected procedure 100 may allow for further analysis for procedures tooccur. As discussed above, the selected segmented (i.e. identified)tracts may be used for selected procedures, such as tumor resection,implant positioning, or other appropriate determinations. Therefore,after or in the ending block 150, the user 78 may identify a specificlocation or tracts relative or near a tumor for resection of the tumor.Further, the user 78 may identify tracts relative to a tumor or otherphysiological anomaly that may affect (e.g. move) the tract, in thesubject 24, from generally known or identified tracts to assist inpositioning an implant in the subject 24, or the like.

In various embodiments, the output segmented fiber tracts in block 140may be displayed on the display device 36, such as relative to an imageof the subject 24 (e.g. a 3D MRI of the subject 24) to assist in aprocedure. As discussed above, the image 32 may be registered to thesubject 24. Accordingly, the output segmented fiber tracts from block140 may be superimposed on the image 32 to assist in performing aprocedure on the subject 24. The icon or graphical representation 94 ofthe instrument 76 may, therefore, also be displayed relative to theoutput segmented fiber tracts from block 140. It is understood that theoutput segment fiber tracts may also be displayed as a graphicalrepresentation superimposed on the image generated from the image dataof the subject 24 and displayed as the image 32.

The method 100, therefore, may be used to automatically segment givenfiber tracts, such as in a brain of the subject 24. The segmented fibertracts may be used to assist in a procedure, such as in the end block150. The procedure may be a navigated procedure to for implantation of alead (e.g. deep brain stimulation lead), a resection of a tumor, etc.The automatic segmentation may be performed by the processor module 48by evaluating the image tractography with the recalled classificationsystem.

Turning reference to FIG. 4, and with continuing reference to FIG. 1 andFIG. 2, a process for identifying or segmenting neuronal fiber tracts isillustrated in method 200. The method 200 may include additionalfeatures, in addition to those discussed above in relation to the method100, and/or may include steps or processes similar or identical to thosediscussed above. Accordingly, similar processes will be included withthe similar or same reference numerals, augmented by a prime C) and willnot be discussed in detail here. It is understood that the processes soidentified may be identical to those discussed above and/or may besimilar and include slight noted augmentations related to the specificmethod illustrated in the process 200.

Initially, the method 200 may begin in start block 204, which is similarto the start block 104, as discussed above. The method 200 may thenproceed to access and/or recall diffusion weighted gradient images inblock 108′. The accessed or recalled diffusion weighted gradient imagesmay be similar to those discussed above in block 108. The diffusionweight images may then have selected tractographies determined in block112′. The selected image tractography may be similar to the imagetractography in block 112, as discussed above. Further, the imagetractography may then be output in block 116′. Again, the output of theselected image tractography may be similar to the image output in block116, as discussed above.

The process of accessing or recalling diffusion weighted images in block108′, image tractography in block 112′, and an output of imagetractography in block 116′ may be similar to that as discussed above.Therefore, the details thereof will not be repeated here and may bereferred to above.

The process 200, however, may further include additional and/oralternative processes, including generating a noisy diffusiontractography based on the selected tractography form block 112′. Thenoisy tractography may be generated with various additional inputs, asdiscussed herein. The noisy diffusion tractography, therefore, mayinclude and/or be the result of a selected tractography algorithm thatidentifies the possible tracts due to the diffusion weight informationcollected in the image data accessed in block 108′.

The method 200 may also include a second or secondary input path orportion 220. The sub-process, in various embodiments, may be an inputwith the accessed diffusion weighted gradient images in block 108′. Thesecondary input may assist in identifying regions of interest (ROIs)within the at least selected portions, including the, image, such aswithin the brain. The input sub-process 220 may include MR images (whichmay include FA images) may be accessed in block 224. The MR imagesaccessed in block 224 may be images that are acquired with the diffusionweighted gradient images in block 108′. As is understood by one skilledin the art, the diffusion weighted gradient images may be acquired as aprocess of acquiring images with an MRI system. Accordingly the MRimages may be acquired substantially simultaneously with the diffusionweighted gradient images accessed in block 108′.

The sub-process 220 may further include a machine learning system, suchas an artificial neural network (ANN) which may include a convolutionalneural network (ANN e.g. CNN), that is trained may be recalled in block226. The trained ANN may be trained with appropriate image data, such asa training set of image data of the same region or portion imaged in theaccessed MR images in block 224. For example, the ANN may be trainedwith classified or identified regions, such as ROIs that may be withinthe access MR image. Various ROIs may include anatomical or featureregions or constructs that relate to beginning and ending regions ofknown or identified given fiber neural tracts. They may also includeregions that the tract is known to pass or not pass through. SelectedROIs may include starting regions of the corticospinal tract in thebrainstem (e.g. parts of the cerebral peduncle), ending regions of thecorticospinal tract in the pre-central and post-central gyrus.

The trained ANN, therefore, that is trained on prior image data, may bestored with appropriate weights for various neurons in the ANN foridentifying ROI's in new images. For example, the accessed MR images inblock 224 may be of the subject 24. The trained ANN may be used,therefore, to identify ROI's in the accessed MR images from block 224 inblock 230. The application or identification of ROI's in the MR imagesin block 230 may assist in identifying ROI's of beginning and endingregions of neuronal tracts. The identified ROI's may assist inidentifying given or selected neuronal tracts to assist in segmentingneuronal tracts in the method 200. Output of segmented brain regions orROI's may be made in block 240. The output segmented brain regions mayinclude starting or ending ROI's, such as identified in a training imagedata for the CNN, or any appropriate brain regions. They may alsoinclude regions that the tract is known to pass or not pass through.Other appropriate brain regions may include selected anatomical orphysiological brain regions such as the pre-central and post-centralgyrus, cerebral peduncle, primary visual cortex, lateral geniculatenucleus.

As discussed above, in method 100, a classification system may be usedto automatically identify, also referred to as classify, tracts (e.g.neuronal tracts) from diffusion weighted gradient images. Accordingly,the method 200 may also recall a trained classification system in block120′. The recalled trained classification system may be similar to thatdiscussed above in the recalled training classification system in block120 in method 100, but it may have inputs from sub-process 220.

The sub-process 220 may also or alternatively be an input to the or withthe recalled trained classification system in block 120′. Thus, thesub-process 220 may be used as an input with the process 200. Thesub-process 220 allows the segmentation to include selected informationor segmentation regarding ROIs.

The recalled trained classification system may be used to automaticallyidentify or segment image tract in block 130. The automaticidentification of the image tracts may be similar to that discussedabove in block 130. As illustrated in the method 200, the sub-process220 may also be a separate input for the automatic segmentation in block130′. The output segmented regions in block 240, therefore, may also oralternatively be input to the automatic segmentation block 130′.

The segmentation of given neuronal tracts in block 130′ may be performedautomatically by execution of the trained classification system recalledin block 120′. The identification of or segmentation of selected regionsin the image, such as a brain, from block 240 may assist or providebeginning and ending points of neuronal tracts, or other appropriateregions in the image. The other image regions may assist in theclassification system in identifying and classifying the neural tracts.As discussed above, the neuronal tracts may be identified given variouscharacteristics, and selected beginning and ending regions (or otherregions that tract passes or does not pass through) may assist inidentifying the neuronal tracts as the fiber tracts in the images.Nevertheless, the identification of selected regions in the image andsegmentation thereof, may assist in the identification and segmentationof the given neuronal tracts in block 130′.

Thus, the method 200 may include various alternative or additionalprocesses. For example, the method 200 may automatically segment inblock 130′ with the input from the sub-process 220 that identifies theROIs in the image.

The segmented neuronal tracts may then be output in block 140′, similarto the output in block 140, as discussed above. The process 200 may thenend in block 150′, also similar to the end in block 150 above.Accordingly, the output of the segmented neuronal tracts may include anyappropriate output and the ending of the process 200 may include variousadditional or separate procedures, as discussed above. Nevertheless, themethod 200, that again may be executed with the processor module 48, asdiscussed above, may include segmentation of selected image regions andthe output thereof in block 240 from the sent process or input 220 toassist in identification, automatically, of neuronal tracts in theaccessed diffusion weighted images from block 108′.

Turning reference to FIG. 5 and with continued reference to FIGS. 1 and2, a method and process 300 for segmenting fiber tracts is discussed.The method 300 may include various portions or features that are similarto the portions and features discussed above in the methods 100 and 200.Like blocks will be identified with similar numerals augmented with adouble prime (″) and will not be discussed in detail here again.Accordingly, the method 300 may be used to segment, includingidentifying, given neuronal tracts.

Initially the method 300, similar to the method 100 and the method 200,discussed above, may be executed by an instruction in a selectedalgorithm or program by a selected processor, such as the processormodule 48. Accordingly, it is understood that the method 300 may beperformed by the workstation or processor system 40. The method 300 maybe included in an algorithm that may be performed or carried out by aprocessor to assist and/or efficiently carry out a selected procedure,as discussed further herein.

Accordingly, the method 300 may begin in start block 304. Starting inblock 304 may be similar to that as discussed above, such as in block104. Accordingly various operations may be used to begin the process300. After starting in block 304, a recall or accessing of diffusionweighted gradient image data may be made in block 108″. After accessingthe recalled diffusion weighted images a tractography of the image maybe made in the block 112″. The image tractography may be similar to theentire image tractography as discussed above in blocks 112 and 112′. Theimage tractography may then be output as an image (e.g. brain)tractography in block 116″.

The process 300, however, may further include additional and/oralternative processes, including generating a noisy diffusiontractography based on the selected tractography from block 112″. Thenoisy tractography may be generated with various additional inputs, asdiscussed herein. The noisy diffusion tractography, therefore, mayinclude and/or be the result of a selected tractography algorithm thatidentifies the possible tracts due to the diffusion weight informationcollected in the image data accessed in block 108″.

As also discussed above in the method 200, the selected sub-method orprocess 220″ may be used to assist in identifying various ROI's in theimage. The sub-process 220″ may be similar to that as discussed above,and will be briefly recited here. Accordingly, MR images may be accessedor recalled in block 224″. A recalled trained ANN may be made in block226″, as discussed above for block 226. The trained ANN may be similarto that discussed above in block 226 and, therefore, may be trained withselected images to identify various ROI's in an image.

The trained CNN may be applied to evaluate the accessed images in block230″. Thereafter, output segmented regions, such as selected ROI'sincluding beginning and endings of selected fiber tracts, may be made inblock 240″. The output segmented ROI's may then be input to the noisydiffusion in 210″ to assist in identifying selected regions forclassification or identifying specific or given neuronal tracts in animage.

The method 300 may include recalling a trained machine learning system,such as one or more artificial neural networks (ANN) (e.g. a generativeadversarial network) in block 320. The trained machine learning systemmay include an artificial neural network that is trained to segmenttract for given neuronal tracts in an image tractography. The trainedmachine learning system may be trained in any appropriate manner, andinclude appropriate weights to adjust input to an activation function toprovide an appropriate output. Generally, the weights may be applied tovarious inputs used to calculate an activation function. Upon theappropriate value, the activation function is activated to provide anoutput for assisting and determining a selected feature. The trainedmachine learning system may, therefore, be trained on selected inputs,such as inputs of identified tracts. The identified tracts may beidentified by a selected user, such as a neurosurgeon, that identifiedtracts in a training data. The machine learning system may then betrained on the training data and then saved to be applied to additionalinput data, including the accessed images in the entire or imagetractography, as discussed above. It is understood, however, thatvarious machine learning systems may include semi- or un-supervisedmethods.

Accordingly, after recalling the trained machine learning system inblock 320, automatic segmentation of the given fiber neuronal tracts maybe made in block 330. The automatic segmentation in block 330 uses thetrained machine learning system to identify the specific or given tractsin the image. The automatic segmentation in block 330 may, again, beperformed by the processor module 48 that includes executinginstructions of the recalled trained ML from block 320 and otherselected inputs.

Also, the sub-process 220″ may optionally provide an input with thetrained machine learning system in block 320 for the automaticsegmentation 330. In other words, the segmented ROI's output in block240″ may or may not be in put for the selected segmentation of the imagetractography from block 116″ automatic segmentation in block 330. Thus,the automatic segmentation may or may not be made with the assistance ofthe segmented ROI from the sub-process 220″.

After the automatic segmentation in block 320, the automaticallysegmented fiber tracts for given neuronal tracts may be output in block140″. The output may be similar to that discussed above in blocks 140,140′. Thereafter, the method 300 may end in block 150″. Again, asdiscussed above, the ending of the process 300 in block 150″ may lead tovarious additional procedures or processes, such as using the segmentedneuronal tracts for a selected procedure, such as a resection of atumor, implantation of a lead, or other appropriate procedure.

The various methods, including method 100, method 200, and method 300,as discussed above, include general features that may be included in analgorithm to segment fiber tracts in a selected image data set. Asdiscussed above, the methods may include various features or processesthat may be carried out by a selected system, such as the processormodule 48, when performing the selected method. With reference to FIGS.3-5, therefore, and with additional reference to FIG. 6A, an algorithmor method 500 is illustrated for assisting in training a system forautomatic segmentation, as discussed above. The method 500 may beincorporated into the processes discussed above, including the methods200, 300, and 400 to generate or allow for segmentation of given fibertracts according to the selected methods, as discussed above. In variousembodiments, one or more of the processes of the method 500 may beincluded in the methods 100, 200, 300 discussed above.

Generally, therefore, the method 500 may include accessing diffusiongradient images in block 510. The accessed gradient diffusion images inblock 510 may be similar to those as discussed above. The accessedgradient diffusion images may then be processed to determine atractography in the block 514 in the accessed image 510. Thetractography processing in block 514, may also be similar to that asdiscussed above, including various tractography algorithms to identifytracts or possible tracts within the images accessed in block 510.Selected specific fibers may be identified in a tractography, accordingto the method 500, in line with the methods 200-400, as discussed above.

In the method 500, various processing may occur to the tractographyprocessed image data may be output and/or accessed after or from thetractography processing in block 514. For example, a directionallyencoded collar (DEC) fractional anisotropy (FA) map may be generatedaccessed in block 520. The DEC/FA image may include one that isidentified based upon the accessed image data according to generallyknown techniques. In addition, a BO mask image in block 524 and a BOimage in block 528 may be accessed and applied to the tractographyprocessing. The BO mask and image may include images with no diffusioninformation included therein. Further, a selected image (e.g. brain)tractography, which may include a whole brain tractography (WBT) inblock 532 may also output from the tractography processing.

The tractography outputs may be, optionally, registered to one orreference images in block 536. If registered, the registered image mayalso transformed with the DEC/FA. The registration to the referencespace may include appropriate scaling and spatial transformation inblock 536. The registration to the reference space may allow for adetermination of a position or various feature of the identified imagetractography.

A creation or determination of negative tracts may be made in block 540.Negative tracts are those fibers in the image tractography that are notpart of the neuronal tracts that the system is trained toclassify/segment. Positive tracts are those fibers belonging to theneuronal tracts that the system is trained to classify/segment, and theyare created by a trained expert (e.g. a neuroradiologist). The creationof the negative tracts at 540 may be generated or created, such as byfiltering tracts farthest in distance and most different in shape fromthe positive tracts. Various additional processing may occur, such asthe negative tracts and positive tracts may also then be augmented inblock 546. Augmentation may include random rotation, scaling,translation, and the addition of a Gaussian and other types of noise tothe fiber and image features.

The tracts in the data, as discussed above, may then be analyzed andprocessed for training of selected segmentation methods of the giventracts. The segmentation training may occur according to varioustechniques, which may occur in tandem or separately. Thus, training mayoccur at least two paths, together or alternatively, to train selectedsystems.

In various embodiments, a classification system may be trained, thus,path 564 may be followed to compute various features of each line inblock 560. The features can include various determinations or analysesof the various tracts, which may also be referred to as lines, forfurther segmentation and identification into fiber tracts as discussedabove, and further herein. For example a number of points and locationof points along the tract line may be determined. Vectors of each linesegment between points may be determined, such as relative to each otherand from each of the respective points. Fractional anisotropy (FA)values may be determined or calculated for each point. Alsodirectionally encoded color information may be calculated or determinedfor each point. Curvature at each point, such as to each of the adjacentpoints, may also be calculated along each of the tracts. In variousembodiments, an atlas may also be identified or accessed, such as agenerally known brain atlas image or model and comparison to thestructure of the identified tracts may be made. Appropriate brainatlases may include those included in the open-source applicationFreesurfer, or FSL (created by the FMRIB group in Oxford, UK). Furthercomputations may include a length of a tract between a starting regionor a point and/or an end region or point. Accordingly, an entire lengthof a tract may be measured or computed for each of the tracts in theimage.

Input regarding a region of interest sub-process 580 may also be made.The region of interest sub-process 580 may be similar to the region ofinterest sub-process 220, as discussed above. Accordingly, accessing ofanatomical image data in block 584 may be made and a recognition ofvarious regions, such as starting and ending regions, anatomicalfeatures or structures, or the like may be made in block 588. Therecognition of the various features may include the application of amachine learning system, such as that discussed above, including a CNN.The CNN may be trained with various data and the trained CNN may be usedto identify structures or regions in accessed image data, such as theimage data accessed in block 584. Nevertheless, the ROI sub-process 580may be used to identify ROI to assist in a determination or segmentationof selected neuronal fibers. The input from the sub-process 580 may beincluded and/or augment the computed features in block 560.

The sub-process 580 may also access or compare to selected fiber tractatlases in block 592. Atlases may include models of fiber tracts thatmay be used to analyze or process tracts in the segmentation of block514. Thus, the sub-processes 580 may utilize selected atlases of fibertracts.

In the classification system training, a classification system fortraining may be recalled or generated in block 610. The trainingclassification system may include appropriate classification systems,such as those discussed above including a random force or otherappropriate classification system.

During training, or any selected time, the classification system may beused to classify and segment appropriate or given neuronal fibers in theaccessed diffusion gradient image data in block 510. The segmentationmay be performed automatically, as discussed above. The tractographymay, therefore, be classified in block 620 with the recalledclassification system. The classification may be similar to that asdiscussed above, based upon the trained classification systems.

After the classification or segmentation of the fiber tracts, theclassified or segmented fiber tracts belonging to given neuronal tractsmay be output in block 630. The output may be any appropriate output,including those discussed above. The output may include a display of thesegmented neuronal tract, saving of the segmented neuronal tracts foradditional analysis, or other appropriate outputs.

In various embodiments, a second or alternative path 634 may befollowed. The path 634 directs to training a machine learning system forsegmentation in block 636. The machine learning system may include anartificial neural network, such as a convolution neural network. The MLtraining in block 636 may be performed with the input for thetractography in block 514 and may include the optional reference imageregistration. In various additional or alternative embodiments, thetraining the ML may also include the computed features from block 560.Thus, the training the ML in block 636 may include selected inputs, asdiscussed above. The training of the ML may also, therefore, lead tosegmentation of fiber tracts in block 620.

The method 500 may then output and/or save the trained system in block634. In various embodiments, the output may be updated or validated suchas increasing training data and/or outputs may be confirmed or checkedfor training progress. Thus, the trained systems may be saved or storedat a selected time and/or when the systems are appropriate trained inblock 634.

The method 500 may end in block 640. The ending of the process 500 maybe similar to the ending of the processes 200-400, as discussed above.Accordingly, the output from block 630 may be used to assist inidentifying appropriate fiber tracts in a selected subject, such as thesubject 24. The segmented fiber tracts may be used to assist in variousprocedures, such as a resection of a tumor, implantation of a device, orother appropriate procedures. Accordingly, the output 630 may be used toassist in performing a procedure and/or planning a procedure on aselected subject, such as a human subject for a selected procedure.

The method 500 may be a training process to train selected systems, suchas a classification or machine learning system for segmentation of fibertracts. The training system may then be used at a selected time tosegment fiber tracts, such as according to methods as discussed above.Generally, with reference to FIG. 6B, a method 700 may be used tosegment the data to segment fiber tracts.

The segmentation method 700, may be similar to the training method 500,and similar or identical blocks will use the same reference numberaugmented with a prime (′). The segmentation method 700, however, mayinclude or utilize the trained systems to segment fiber tracts, asdiscussed above. Therefore, the method 700 may include accessingdiffusion gradient images in block 510′. The accessed gradient diffusionimages in block 510′ may be similar to those as discussed above. Theaccessed gradient diffusion images may then be processed to determine atractography in the block 514′ in the accessed image 510′. Thetractography processing in block 514′, may also be similar to that asdiscussed above, including various tractography algorithms to identifytracts or possible tracts within the images accessed in block 510′.Selected specific fibers may be identified in a tractography, accordingto the method 700, in line with the methods 200-400, as discussed above.

In the method 700, various processing may occur to the tractographyprocessed image data may be output and/or accessed after or from thetractography processing in block 514′. For example, a directionallyencoded collar (DEC) fractional anisotropy (FA) map may be generatedaccessed in block 520′. The DEC/FA image may include one that isidentified based upon the accessed image data according to generallyknown techniques. In addition, a BO mask image in block 524′ and a BOimage in block 528′ may be accessed and applied to the tractographyprocessing. The BO mask and image may include images with no diffusioninformation included therein. Further, a selected image (e.g. brain)tractography, which may include a whole brain tractography (WBT) inblock 532′ may also output from the tractography processing.

The tractography outputs may be, optionally, registered to one orreference images in block 536′. If registered, the registered image mayalso transformed with the DEC/FA. The registration to the referencespace may include appropriate scaling and spatial transformation inblock 536′. The registration to the reference space may allow for adetermination of a position or various feature of the identified imagetractography.

In various embodiments, a classification system may be used to segmentthe fiber tracts, thus, path 564′ may be followed to compute variousfeatures of each line in block 560′. The features can include variousdeterminations or analyses of the various tracts, which may also bereferred to as lines, for further segmentation and identification intofiber tracts as discussed above, and further herein. For example anumber of points and location of points along the tract line may bedetermined. Vectors of each line segment between points may bedetermined, such as relative to each other and from each of therespective points. Fractional anisotropy (FA) values may be determinedor calculated for each point. Also directionally encoded colorinformation may be calculated or determined for each point. Curvature ateach point, such as to each of the adjacent points, may also becalculated along each of the tracts. In various embodiments, an atlasmay also be identified or accessed, such as a generally known brainatlas image or model and comparison to the structure of the identifiedtracts may be made. Appropriate brain atlases may include those includedin the open-source application Freesurfer, or FSL (created by the FMRIBgroup in Oxford, UK). Further computations may include a length of atract between a starting region or a point and/or an end region orpoint. Accordingly, an entire length of a tract may be measured orcomputed for each of the tracts in the image.

Input regarding a region of interest sub-process 580′ may also be made.The region of interest sub-process 580′ may be similar to the region ofinterest sub-process 220, as discussed above. Accordingly, accessing ofanatomical image data in block 584′ may be made and a recognition ofvarious regions, such as starting and ending regions, anatomicalfeatures or structures, or the like may be made in block 588′. Therecognition of the various features may include the application of amachine learning system, such as that discussed above, including a CNN.The CNN may be trained with various data and the trained CNN may be usedto identify structures or regions in accessed image data, such as theimage data accessed in block 584′. Nevertheless, the ROI sub-process580′ may be used to identify ROI to assist in a determination orsegmentation of selected neuronal fibers. The input from the sub-process580′ may be included and/or augment the computed features in block 560′.

The sub-process 580′ may also access or compare to selected fiber tractatlases in block 592′. Atlases may include models of fiber tracts thatmay be used to analyze or process tracts in the segmentation of block514′. Thus, the sub-processes 580′ may utilize selected atlases of fibertracts.

In the classification system, a trained classification system may berecalled in block 610′. The trained classification system may includeappropriate classification systems, such as those discussed aboveincluding a random force or other appropriate classification system.

The trained classification system may be used to classify and segmentappropriate or given neuronal fibers in the accessed diffusion gradientimage data in block 510. The segmentation may be performedautomatically, as discussed above. The tractography may, therefore, beclassified in block 620′ with the recalled classification system. Theclassification may be similar to that as discussed above, based upon thetrained classification systems.

After the classification or segmentation of the fiber tracts, theclassified or segmented fiber tracts belonging to given neuronal tractsmay be output in block 630′. The output may be any appropriate output,including those discussed above. The output may include a display of thesegmented neuronal tract, saving of the segmented neuronal tracts foradditional analysis, or other appropriate outputs.

In various embodiments, a second or alternative path 634′ may befollowed. The path 634′ directs to a trained machine learning system forsegmentation in block 636′. The machine learning system may include anartificial neural network, such as a convolution neural network. The MLtraining in block 636′ may be performed with the input for thetractography in block 514′ and may include the optional reference imageregistration. In various additional or alternative embodiments, thetraining the ML may also include the computed features from block 560′.Thus, the training the ML in block 636′ may include selected inputs, asdiscussed above. The training of the ML may also, therefore, lead tosegmentation of fiber tracts in block 620′.

The method 700 may also provide output in block 630′. The output fromblock 630′ may be used to assist in identifying appropriate fiber tractsin a selected subject, such as the subject 24. The segmented fibertracts may be used to assist in various procedures, such as a resectionof a tumor, implantation of a device, or other appropriate procedures.In various embodiments, an instrument may be navigated in the relativeto the fiber tracts. Also, ablation planning and tracking may beperformed. Accordingly, the output 630 may be used to assist inperforming a procedure and/or planning a procedure on a selectedsubject, such as a human subject for a selected procedure. The process700 may also then end in block 640′, similar to the ending of theprocesses 200-400, as discussed above.

In light of the above, a selected system may automatically segmentappropriate or given fiber tracts in an accessed diffusion gradientimage data. In various embodiments, the data may be data relating tonerves or nerve bundles (e.g. brain data) and, therefore, the tracts mayalso be referred to as fiber neuronal tracts in a brain image. Theautomatic segmentation may be performed by a selected processor moduleto identify fiber tracts within a selected subject. The identifiablefiber tracts may include any appropriate fiber tracts such as opticradiations, an arcuate fasciculus (AF), a superior longitudinalfasciculus (SLF), a corticospinal tract (CST), frontal aslant tract(FAT), a fornix tract, a dentato rubro thalamic tract (DRT), or anyother appropriate tracts. Nevertheless, the automatic segmentation mayallow for minimal or no user intervention and identify relevant fibertracts in a subject.

Example embodiments are provided so that this disclosure will bethorough, and will fully convey the scope to those who are skilled inthe art. Numerous specific details are set forth such as examples ofspecific components, devices, and methods, to provide a thoroughunderstanding of embodiments of the present disclosure. It will beapparent to those skilled in the art that specific details need not beemployed, that example embodiments may be embodied in many differentforms and that neither should be construed to limit the scope of thedisclosure. In some example embodiments, well-known processes,well-known device structures, and well-known technologies are notdescribed in detail.

In one or more examples, the described techniques may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored as one or more instructions orcode on a computer-readable medium and executed by a hardware-basedprocessing unit. Computer-readable media may include non-transitorycomputer-readable media, which corresponds to a tangible medium such asdata storage media (e.g., RAM, ROM, EEPROM, flash memory, or any othermedium that can be used to store desired program code in the form ofinstructions or data structures and that can be accessed by a computer).

Instructions may be executed by one or more processors, such as one ormore digital signal processors (DSPs), general purpose microprocessors,graphic processing units (GPUs), application specific integratedcircuits (ASICs), field programmable logic arrays (FPGAs), or otherequivalent integrated or discrete logic circuitry. Accordingly, the term“processor” as used herein may refer to any of the foregoing structureor any other physical structure suitable for implementation of thedescribed techniques. Also, the techniques could be fully implemented inone or more circuits or logic elements.

The foregoing description of the embodiments has been provided forpurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure. Individual elements or featuresof a particular embodiment are generally not limited to that particularembodiment, but, where applicable, are interchangeable and can be usedin a selected embodiment, even if not specifically shown or described.The same may also be varied in many ways. Such variations are not to beregarded as a departure from the disclosure, and all such modificationsare intended to be included within the scope of the disclosure.

It should be understood that various aspects disclosed herein may becombined in different combinations than the combinations specificallypresented in the description and accompanying drawings. It should alsobe understood that, depending on the example, certain acts or events ofany of the processes or methods described herein may be performed in adifferent sequence, may be added, merged, or left out altogether (e.g.,all described acts or events may not be necessary to carry out thetechniques). In addition, while certain aspects of this disclosure aredescribed as being performed by a single module or unit for purposes ofclarity, it should be understood that the techniques of this disclosuremay be performed by a combination of units or modules associated with,for example, a medical device.

What is claimed is:
 1. A method to segment neuronal tracts in an image,comprising: accessing selected data of a subject; evaluating theaccessed selected data to determine an image tractography including aplurality of tracts; evaluating at least a sub-plurality of tracts ofthe plurality of tracts with a trained machine learning algorithm;determining whether at least one of the evaluated sub-plurality oftracts is a given fiber neuronal tract based at least on the evaluationof at least the sub-plurality of tracts of the plurality of tracts; andoutputting which at least one tract of the plurality of tracts is/arethe given fiber neuronal tract when at least one of the evaluatedplurality of tracts is determined to be the given fiber neuronal tract.2. The method of claim 2, wherein the accessed selected data includesdiffusion weighted gradient images.
 3. The method of claim 3, whereinevaluating the accessed selected data based on the first criteria todetermine the image tractography including the plurality of tractsincludes: determining an anisotropy of water within the selected data;and determining tracts through an image in the selected data based onthe determined anisotropy.
 4. The method of claim 1, wherein evaluatingall of the sub-plurality of tracts includes at least one of: determiningpoints along each tract of the sub-plurality of tracts; evaluating afractional anisotropy at each point of the determined points; evaluatinga diffusion-encoded-color at each point of the determined points;determine a distance from a starting region of each tract to an endingregion of each tract; and determine a curvature of the tract at eachpoint of the determined points.
 5. The method of claim 1, furthercomprising: determining selected regions of interest relative to theaccessed selected data with a recalled trained convolutional neuralnetwork.
 6. The method of claim 5, further comprising: whereinevaluating the accessed selected data to determine an image tractographyincludes evaluating an entire image to determine a plurality of tracts.7. The method of claim 6, further comprising: recalling the trainedconvolutional neural network; accessing anatomical image data of thesubject; and determining at least one region of interest in the accessedimage data with the recalled trained convolutional neural network. 8.The method of claim 7, wherein determining the selected regions ofinterest relative to the accessed selected data with the recalledtrained convolutional neural network includes identifying the determinedat least one region of interest determined in the accessed image data inthe accessed selected data of the subject.
 9. The method of claim 8,wherein the at least one region of interest includes at least one of astarting region of the given fiber neuronal tract or an ending region ofthe given fiber neuronal tract.
 10. The method of claim 8, furthercomprising: determining at least one tract of at least the sub-pluralityof tracts that interacts with the determined at least one region ofinterest; wherein evaluating at least the sub-plurality of tracts of theplurality of tracts includes evaluating only the determined at least onetract.
 11. The method of claim 5, further comprising: recalling thetrained convolutional neural network; accessing anatomical image data ofthe subject; and determining at least one region of interest in theaccessed image data with the recalled trained convolutional neuralnetwork.
 12. The method of claim 11, wherein determining the selectedregions of interest relative to the accessed selected data with therecalled trained convolutional neural network includes identifying thedetermined at least one region of interest determined in the accessedimage data in the accessed selected data of the subject.
 13. The methodof claim 12, wherein evaluating at least the sub-plurality of tracts ofthe plurality of tracts with the recalled trained classification systemincludes evaluating the sub-plurality of tracts based on the determinedat least one region of interest determined in the accessed image data.14. The method of claim 1, wherein the given fiber neuronal tract is atleast one of a cortico-spinal tract, an optical tract, a frontal aslanttract, a dentato rubro thalamic tract, a fornix tract, or combinationsthereof.
 15. The method of claim 1, further comprising: training amachine learning algorithm to identify the given fiber neuronal tract togenerate the trained machine learning algorithm with at least trainingdata including the given fiber neuronal tract identified therein. 16.The method of claim 1, further comprising: navigating an instrumentrelative to the given fiber neuronal tract.
 17. A system configured tosegment given neuronal tracts in an image of a subject, comprising: aprocessor system configured to execute instructions to: access aselected data of a subject; evaluate the accessed selected data based ona first criteria to determine an image tractography including aplurality of tracts; evaluate at least a sub-plurality of tracts of theplurality of tracts with a trained machine learning algorithm; determinewhether at least one of the evaluated sub-plurality of tracts is a givenfiber neuronal tract based at least on the evaluation of at least thesub-plurality of tracts of the plurality of tracts; and output which atleast one tract of the plurality of tracts is/are the given fiberneuronal tract when at least one of the evaluated plurality of tracts isdetermined to be the given fiber neuronal tract.
 18. The system of claim17, wherein the processor system is configured to execute furtherinstructions to determine selected regions of interest relative to theaccessed selected data with a recalled trained convolutional neuralnetwork; wherein the determination of whether at least one of theevaluated sub-plurality of tracts is a given fiber neuronal tract isfurther based on the determination of selected regions of interest. 19.The system of claim 18, further comprising: a memory system configuredto store the instructions.
 20. The system of claim 17, furthercomprising: a display device configured to display an image of thesubject and the outputted given fiber neuronal tract.
 21. The system ofclaim 17, further comprising: an imaging system configured to acquirediffusion weighted gradient images of the subject.
 22. The system ofclaim 17, further comprising: a navigation system including a trackingsystem and a tracking device; wherein an instrument is operable to benavigated relative to the output given neuronal tract within thenavigation system.